Deciphering the transcriptional regulatory network (TRN) governing a biological process in mammalian systems is essential to our understanding of basic mechanisms underlying normal physiology as well as disease etiology. It is a daunting task because too many links in the TRN are unknown. The emergence of ChIP-chip and ChIP-seq technologies has enabled the mapping of the genome-wide binding sites of many transcription factors (TFs) known to be key regulators in a biological process. However, these two technologies are limited to the known regulators with ChIP-quality antibodies. We found that TF binding is often associated with a dynamic histone mark signature and can be computationally predicted from the genome-wide histone mark dynamics. Therefore, we hypothesize that with time-course nucleosome-resolution ChIP-seq of a few informative histone marks and RNA-seq data of gene expression, and effective computational modeling, we could infer the TRNs in mammalian biological processes. Specifically, we propose to develop effective computational algorithms to achieve Aim1: first, predict TF binding from nucleosome-resolution histone mark dynamics; second, identify target genes from TF binding, histone marks and gene expression profiles; and third, infers the TRN over a time course. We also propose to apply the above algorithms in two biological systems in Aim 2. One is the mouse myoblast cell line C2C12 differentiation into bone, fat, or muscle, and the other is the human apocrine breast cancer cell line MDA-MB-453 reversible reprogramming to epithelial cells with vitamin D treatment. Through time-course nucleosome-resolution histone mark ChIP-seq and RNA-seq profiling, we will computationally infer and experimentally validate the TRNs in these two systems.